NDLI: An evaluation of corpus-driven measures of medical concept similarity for information retrieval

Content Provider	ACM Digital Library
Author	Bruza, Peter Koopman, Bevan Lawley, Michael Zuccon, Guido Sitbon, Laurianne
Abstract	Measures of semantic similarity between medical concepts are central to a number of techniques in medical informatics, including query expansion in medical information retrieval. Previous work has mainly considered thesaurus-based path measures of semantic similarity and has not compared different corpus-driven approaches in depth. We evaluate the effectiveness of eight common corpus-driven measures in capturing semantic relatedness and compare these against human judged concept pairs assessed by medical professionals. Our results show that certain corpus-driven measures correlate strongly (approx 0.8) with human judgements. An important finding is that performance was significantly affected by the choice of corpus used in priming the measure, i.e., used as evidence from which corpus-driven similarities are drawn. This paper provides guidelines for the implementation of semantic similarity measures for medical informatics and concludes with implications for medical information retrieval.
Starting Page	2439
Ending Page	2442
Page Count	4
File Format	PDF
ISBN	9781450311564
DOI	10.1145/2396761.2398661
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2012-10-29
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Semantic similarity Medical information retrieval
Content Type	Text
Resource Type	Article

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An evaluation of corpus-driven measures of medical concept similarity for information retrieval